The team from Maluuba, a Canadian deep learning startup acquired by Microsoft earlier this year, used Reinforcement Learning to play the Atari 2600 version of Ms. Pac-Man perfectly, achieving the maximum score possible of 999,990.

Rushin Shah left his post as a senior machine learning manager on Apple’s virtual assistant to join Facebook’s Applied Machine Learning team, where he’ll be working on natural language and dialog understanding.

In Schema Networks, knowledge about the world is learned as small graphical model fragments called schemas. These schemas represent what they learn in terms of entities (think nouns), their attributes (adjectives), and interactions between entities (verbs).

An extremely detailed guide to what a career in AI policy and strategy looks like. It includes concrete questions that need to be answered, differences between practitioners and researchers, and an extensive list of resources.

This post walks you through building a Bayesian linear regression model, a Bayesian linear regression model with random effects, and a neural network with random effects using the probabilistic programming library Edward.

The FAIR researchers studied negotiation on a multi-issue bargaining task. Two agents are both shown the same collection of items (say two books, one hat, three balls) and are instructed to divide them between themselves by negotiating a split of the items. Both code and research paper are available.

This approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than one percent of our agent’s interactions with the environment.

A new network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. The model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results.

For each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. The authors present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task.